The Role of Data Science in transforming the BFSI Sector By Janifha Evangeline

The Role of Data Science in transforming the BFSI Sector

Janifha Evangeline | Wednesday, 09 February 2022, 11:01 IST

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The implementation of data science is set to revolutionize the BFSI sector rapidly. Therefore, banks are looking for ways that would help them in understanding their customers better. This would lead to increased customer loyalty by rendering high operational efficiency.

Banks are also in the process of identifying patterns in a large amount of the transaction data available to interact efficiently with customers. Although extracting insights from such a huge amount of data is a daunting and challenging task owing to the unstructured format of data, banks are implementing data science to deal with this.

“Data Science allows businesses to use the data tools available to them, regardless of whether it is consumer data, operational data, or anything similar, in a smart manner. It also helps the BFSI sector to enter new customers, cross-sell goods and services across successful distribution networks, improve customer satisfaction, etc.,” says Alok Bansal, MD & Country Head, Visionet India.

“Data science is ubiquitously applicable to any field of finance, whether it is risk assessment, pricing, advertisement outreach, customer outreach, product creation, expense, and revenue distribution,” he adds.

Applications of Data Science in banking

Fraud detection to minimize losses

Ensuring the absolute security of customers is one of the greatest concerns of banks. Since fraud detection is a very crucial matter, banks are finding numerous ways that can help in detecting fraud in the earlier stages. And, this will help in minimizing losses as well.

Therefore, when banks implement Data science it not only helps in achieving the required level of protection but it is also helping them to avoid financial losses. Data scientists will help in improving the level of customer security and this can be achieved by monitoring & analyzing the various types of banking activities of clients so that they can detect any suspicious/malicious activity.

Data scientists should work on various data mining techniques that include classification, association, clustering, and many others not only for working with different datasets but also to extract meaningful insights and these insights can later be applied to real-time banking problems.

For instance, let us examine a system that holds numerous transactions and if suddenly a large number of transactions occur from a client’s account until the owner of the account himself verifies them. Such systems help the clients to monitor their account activities.

Managing customer data

In today’s era, banks possess huge datasets. However, to manage, collect, analyze and store such a large amount of data is not easy. Therefore, numerous banking organizations are implementing different tools as well as techniques from ML & data science to transform this data into such a format that can be implemented for getting to know their clients better for coming up with new strategies for better revenue generation.

Also, every day many terabytes of data are being generated owing to the growing usage and popularity of digital banking. While, the data scientists apply numerous methods to extract the data that is useful for them, first, the analysis of this data facilitates them to get insights regarding customer behavior, which will enable the data scientists to develop efficient models that would produce more accurate results. Applying different types of ML algorithms helps banks in deriving new opportunities to generate revenue and make some of the crucial data-driven decisions.

Risk Modelling

The identification as well as evaluation of risks is a matter of great concern for investment banks and to regulate various financial activities as well as decide the right price for numerous financial instruments banks implement Data Science in banking.

The different types of risk modeling include Credit risk modeling & Investment risk modeling.

Credit Risk modeling: By analyzing the previous history as well as credit reports of customers, credit risk modeling will help banks to predict whether a customer will be able to repay their loan.

While the credit risk analysis facilitates the bank to calculate a risk score for every case, it decides whether to sanction the loan or not based on the risk score value.

Investment risk modeling: The reason that makes Risk Modeling so important for investment banks is that they implement investment risk modeling to detect risky investments. This will enable the banks to provide better investment advice to the clients which will, in turn, help the clients make the right decisions for increasing profit.

Now by implementing data science solutions, banks are designing new strategies for effective risk modeling and this will help them in making better data-driven decisions.

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